The present invention relates to a data processing system having a memory packaged therein for realizing a large-scale and fast parallel distributed processing and, more specifically, to a neural network processing system.
The parallel distributed data processing using the neural network called the "neuro-computing" (as will be shortly referred to as the "neural network processing") is noted in the field of acoustics, speech and image processing, as described in either on pp. 145-168, "Parallel networks that learn to pronounce English text. Complex Systems 1 by Sejnowski, T. J., and Rosenberg, C. R. 1987, or "Neural Network Processing" published by Sangyo Tosho and edited by Hideki Asou. In the neural network processing, a number of processing elements called the "neurons" connected in a network exchange the data through transfer lines called the "connections" for high-grade data processing. In each neuron, the data (i.e., the outputs of the neurons) sent from another neuron are subjected to simple processing such as multiplications or summations. Since the processing in the individual neurons and the processing of different neurons can be carried out in parallel, the neural network processing is advantageous in principle in its fast data processing. Since algorithms (or learnings) for setting the connection weights of the neurons for a desired data processing have been proposed, the data processing can be varied for the objects, as described in either pp. 533-536, "Learning representations by back-propagation errors.", Nature 323-9 (1986a) by Rumelhart, D. E., Hinton, G. E. and Williams, R. J., or in 2nd Section of "Neural Network Processing" published by Sangyo Tosho and edited by Hideki Asou.